Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008)
Adaptive Control for Autonomous Underwater Vehicles
Conor McGann, Frederic Py, Kanna Rajan, John Ryan, Richard Henthorn
Monterey Bay Aquarium Research Institute, Moss Landing, California
{cmcgann, fpy, kanna.rajan, ryjo, henthorn}@mbari.org
Abstract
We describe a novel integration of Planning with
Probabilistic State Estimation and Execution. The resulting
system is a unified representational and computational
framework based on declarative models and constraintbased temporal plans. The work is motivated by the need to
explore the oceans more cost-effectively through the use of
Autonomous Underwater Vehicles (AUV), requiring them
to be goal-directed, perceptive, adaptive and robust in the
context of dynamic and uncertain conditions. The novelty of
our approach is in integrating deliberation and reaction over
different temporal and functional scopes within a single
model, and in breaking new ground in oceanography by
allowing for precise sampling within a feature of interest
using an autonomous robot. The system is general-purpose
and adaptable to other ocean going and terrestrial platforms.
Fig. 1: INL mapped by AUV in Monterey Bay, California.
The AUV survey track is shown in gray.
Estimation integrates a number of science observations to
produce a likelihood that the vehicle sensors perceive a
feature of interest. Onboard planning and execution
enables adaptation of navigation and instrument control
based on the probability of having detected such a
phenomenon. It further enables goal-directed commanding
within the context of projected mission state and allows for
replanning for off-nominal situations and opportunistic
science events.
We have tested our system in the coastal ocean where
complex interactions between physical, chemical,
geological, and biological processes often occur. Our
studies have targeted two highly unpredictable phenomena
frequently encountered in the coastal waters of central
California. (1) Intermediate Nepheloid Layers (INL), (Fig
1), are fluid sheets of suspended particulate matter that
originate from the seafloor (McPhee-Shaw 2004, Ryan
2005). (2) Estuarine plumes are outflows from landinfluenced coastal waterways into coastal waters. In these
studies our aim is to dynamically adapt the control of an
AUV after detecting a feature of interest, to take water
samples within the feature and to modify the survey spatial
resolution while projecting the impact to the mission plan.
The novelty of this work is twofold. We integrate
deliberation and reaction over different temporal and
functional scopes within a single agent and a single model
that covers the needs of high-level mission management,
low-level navigation, instrument control, and detection of
unstructured and poorly understood phenomena. Secondly,
we break new ground in oceanography by allowing
scientists to obtain samples precisely within a scientific
feature of interest using an autonomous robot.
The structure of the paper is as follows. We first motivate
our approach by introducing key concepts of our design.
We then describe the synthesis of planning and
probabilistic state estimation within a hybrid executive
based on these ideas. Results from sea trials in Monterey
Bay, California are presented next. We conclude with a
review of related efforts and a discussion of future work.
Introduction
Autonomous Underwater Vehicles (AUVs) (Yuh 2000) are
untethered mobile robotic platforms used by the
oceanographic community. AUVs carry sophisticated
science payloads for measuring important water properties
(Ryan 2005), as well as instruments for recording the
morphology of the benthic environment with advanced
sonar equipment (Thomas 2006). The extensive payload
capacity and operational versatility of these vehicles offer a
cost-effective alternative to traditional ship-based
oceanographic measurements.
Existing mission control practices rely on manually
scripted plans generated a priori that are tedious to
construct and, more importantly, inflexible to changes
during mission execution. This prevents in-situ adaptation
of mission structure essential to improving operation in an
environment as dynamic and uncertain as the ocean
(Rudnick and Perry 2003). Ocean exploration also requires
adaptation to pursue unanticipated science opportunities.
Safe and effective adaptation requires a balanced
consideration of mission objectives, environmental
conditions and available resources. Moreover, as mission
durations increase, sustained presence in the ocean requires
the ability to deal with off-nominal conditions.
We have developed and deployed an onboard Adaptive
Control System that integrates Planning and Probabilistic
State Estimation in a hybrid Executive. Estimation,
Planning, and Execution must be integrated in order to
inform planning systems with predictions of the most
likely evolution of the environment. Probabilistic State
Copyright © 2008, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
1319
Vehicle Control Subsystem (VCS). For purposes of
discussion the VCS is conceptualized as a functional layer.
Each reactor receives goals to drive its behavior, sends
goals to task a lower level reactor, and receives
observations on system
state
variables
of
interest. For example,
the Mission Manager
may receive a goal to
conduct a volume
survey in a designated
region,
with
constraints on finish
time, depth bounds, or
spatial
resolution
ranges. It may generate
a plan at a high level
that includes steps to
navigate to a target
location, turn to a
heading etc. This highlevel plan considers
Fig. 2: Integrated System Design
the full temporal scope
of the mission (e.g., 10 hours). Near-term navigation steps
of the plan are dispatched as goals to the Navigator. The
Navigator will plan over a shorter time horizon (e.g., 60
seconds). Ultimately, mission commands (e.g., ascend, get
a GPS fix, descend) are generated and dispatched to the
VCS. Feedback from the VCS takes the form of vehicle
and command state updates. Fig 2 shows a common
framework for integrating deliberation and reaction for the
Mission Manager and Navigator that we call a
Deliberative Reactor based on the EUROPA2 (Frank 2003)
constraint-based temporal planning library.
Key Concepts
The Sense-Plan-Act (SPA) paradigm for robot control
embeds planning at the core of a control loop. Planning is
typically the dominant cost and can limit the reactivity of
an agent. Furthermore, the world can (and often does)
change at a faster rate than the planner can plan. In this
situation, the agent may thrash if the internal state of the
plan gets out of synch with the actual state of the world.
So, while SPA offers a general representational and
computational framework for control, it is problematic for
application in systems that require extensive deliberation
and fast reaction.
For example, in our domain, an AUV may be tasked with a
set of objectives to be accomplished over the course of a
mission. Correctly selecting a feasible subset of these
objectives, and deciding the order in which they should be
accomplished has a big impact on the utility of a mission.
To do this effectively requires deliberation over the full
temporal extent of the mission. In contrast, instrument
control decisions require faster reaction times (e.g., 1
second) but can be taken with a more myopic view of
implications to the plan without loss in utility. This
suggests that the control responsibilities of the executive
can be factored according to how far to look ahead
(temporal scope), which state variables to consider
(functional scope) and required reaction time (latency). To
exploit this we allow the executive to be partitioned into a
coordinated collection of control loops, each with its own
internal SPA cycle, distinguished explicitly by these
parameters above.
Although partitioning can be a powerful tool to reduce
planning complexity it is unreasonable to require all
planning to complete at the rate at which the environment
is changing. Rather, planning within each control loop
must be completed at the rate at which it must react to
provide effective control. In order to allow a uniform
quantization of time throughout the model, yet permit
different rates of reaction for different control loops, it
becomes necessary to allow sensing and planning to be
interleaved. This allows multiple state updates to occur
during deliberation, keeping the plan in synch with an
evolving world state. To support this we decouple
synchronization of the plan from deliberation over goals
and allow them to be interleaved.
Our emphasis on partitioning and synchronization to
flexibly and efficiently integrate deliberation and reaction
offers a novel variation of the Sense-Plan-Act paradigm.
We now describe the application of these ideas to the
problem of adaptive mission control.
The Plan Database
A constraint-based temporal plan is a form of constraint
network (Mackworth 1992). Algorithms designed to
operate on the plan during planning are directly exploited
in execution to propagate state updates, detect conflicts,
and reason about temporal precedence. A single model is
used to capture the domain details at all levels of
abstraction. The model is applied using a propositional
inference engine triggered by changes in the plan. The
dependency structure of the plan is utilized in execution to
aid recovery from plan failure. The plan, model and plan
manipulation algorithms are contained in the Plan
Database.
Figure 3 illustrates a plan fragment 1432 seconds into
mission execution. It contains two state variables: the
The Teleo-Reactive Executive
The structure of the integrated control system is shown in
Fig 2 implemented as a Teleo-Reactive Executive (T-REX)
where each component is an instance of a Teleo-Reactor
(McGann 2008). Three reactors are organized
hierarchically: a Mission Manager, a Navigator, and a
Fig 3: The plan database at t=1432
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SamplerController and the SamplerBehavior. The former
is an internal state variable of the Navigator, the latter is
external, reflecting the status of a behavior for sampler
activation resident in the VCS. Each reactor is the owner of
its internal state variables, and publishes their values
during synchronization. Each reactor subscribes to
observations on its external state variables and must
reconcile its internal state with these external values. The
evolution of each state variable is recorded (when in the
past) and projected (when in the future) in a timeline. The
SamplerController oscillates between firing and ready.
Each state includes a bound on the number of samples that
can be used (e.g., 2), and a record of the number of
samples taken thus far. The start and end times of each
state are shown. The figure shows that the
SamplerBehavior was activated at t=1431 and remained
active for 1 second. The transition to the Inactive state is
concurrent with the transition of the SamplerController to
a ready state. Notice that this parameter has incremented
after the controller has triggered. Finally, observe that the
end time of the ready state is unbound, since the
SamplerController may remain in a ready state
indefinitely.
To illustrate these ideas further, consider the model rule
below applied to all values of the SamplerController state
variable of the predicate Holds.
the plan as goal-requests to the responsible reactor (e.g.,
the VCS). Dispatching the plan is detailed in (McGann
2008). The goal of synchronization is to produce a
consistent and complete view of the plan at the execution
frontier. Observations are received for external state
variables. For example, say that SamplerBehavior is an
external state variable whose value can be either Active or
Inactive. A message from the VCS indicating that the
behavior
has
completed
becomes
a
SamplerBehavior.Inactive token in the plan database. Fig 5
illustrates the situation before and after this observation is
synchronized.
The observation
is received at
t=1432. The last
observed value
for
the
SamplerBehavior
was active. This
new observation
is resolved by
insertion into the
plan, resulting in
Fig 5: Synchronizing an observation
a restriction of
with the plan
the end time of
the active state.
The connections in the underlying constraint network
propagate the implications of this restriction throughout the
partial plan. This process is repeated for all observations
and for all implications that impact the execution frontier.
In the event that the plan is not consistent with the
observations, the plan is discarded, and the relaxed
execution frontier is synchronized with observations.
∀a ∈ Holds ⇒ ∃ b ∈ Holds
a.start < a.end ^ a.end = b.start
a.max = b.max ^ b.count = a.count +1
A fragment of
the partial plan
where this rule
applies
is
shown in Fig.
4. There are
two values as
before.
However, each
Fig 4: A constraint network fragment
is now depicted
as a token that acts to constrain the set of values a state
variable may hold over some temporal extent. All tokens
have start and end variables. Different predicates may
contain different parameter variables. Bi-directional arrows
indicate constraints between variables in accordance with
the model rule above. The implication from the model is
reflected by the directed arrow from token a to b. Only the
end variable of b is unbound in this example; all other
variables were bound to singletons as the plan is executed.
In general, the ability to leave parts of the plan flexible
supports a least-commitment approach to planning where
only necessary restrictions are imposed allowing final
details to be refined at or close to the point of execution.
The Planner
Automated planning refines an incomplete plan into one
with sufficient detail to support execution. Planning and
execution are interleaved seamlessly while operating on a
shared database. Planning is broken down into atomic
steps, where each step leaves the database in a consistent
state. The planner is invoked one step at a time, in order to
permit synchronization to take place. Updates applied
during synchronization are visible in the next step of the
planner so further refinement of the plan will account for
the changing world. The planner is invoked whenever
flaws occur in the plan. Flaws stem from either externally
Embedding the Plan Database in Execution
The Plan Database is embedded in the control loop by
synchronizing observations with the plan and dispatching
Fig 6: Sub-goal implied by the model to be planned
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requested goals (e.g., from another reactor, or a user-driven
input) or implied by the model and feedback from the
environment.
For example, consider the partial plan depicted in Fig 6.
The Sampler Controller timeline is transitioned to trigger
the sampler, based on observations made from the
environment. In order to actually trigger the instrument, the
model requires the SamplerBehavior to be Active.
Consequently, the model will introduce a sub-goal for the
SamplerBehavior for the planner. Thus, while there is a
clear separation between synchronization and deliberation,
the flow of information through the shared partial plan
structure provides a continuous integration of state as they
are interleaved.
a) HMM Execution trace
Fig 8: Similarity between HMM and Timeline
Representations
The model is applied in execution as follows:
1. Sensor data is classified by the VCS so that it
computes the corresponding cluster as in (Fox 2007).
This cluster value is provided as an observation on
the Cluster timeline to the Navigator.
2. The observation is inserted in the plan during
synchronization.
3. The implications of this observation on the
Estimation timeline, expressed in the model, are
applied and resolved during synchronization.
Constraints then propagate the probability
distribution according to (1).
At this stage we have the estimation deduced from the
HMM. The transition from the estimation to decision
making can then be done directly. For instance to decide
when to take a water sample the steps are:
4. If the probability of being within a feature of interest
(such as an INL) dominates the distribution and other
sampling conditions are satisfied (e.g spatial
constraints dispersing sampling locations), then a
sample should be taken. This rule is evaluated during
synchronization as the constraint network is
propagated.
5. Should this rule fire, a new token will be generated to
transition the SamplerController into a firing state.
This triggers a new deliberation phase to resolve the
implications of this transition (i.e., select a sampling
canister, and generate an action to fire it).
We use a similar process for altering the spatial resolution
of the survey using the HMM to compute the probability of
having seen the feature during the last vehicle transect.
This in turn is used to determine the spatial separation
between the previous and next transect.
State Estimation
To enable the AUV to adapt its mission based on feature
detection, we employ a Hidden Markov Model (HMM)
(Rabiner 1986). To execute this HMM, we encode it
directly within the unified representational and
computational framework of a Deliberative Reactor
allowing for a seamless integration of state estimation
through synchronization and planning. The sensor data we
use depends on the feature of interest; for instance we use
two optical sensor readings for INL detection. HMMs are
useful since the stochastic nature of these models can
correlate the type of features we want to detect with the
sensor observations. And such models can also be learned
using past mission data (Fox 2006).
Fig
7
shows two
hidden
states: in expressing
that
the
AUV is in
Fig 7: The HMM used by the state estimator.
the feature
of interest – and its alternate out. The model specifies the
set of probabilities of observation ci given state si, p(ci | si),
and the probabilities of transition from s’ to s, p(s|s’). In
our implementation the probabilities of transition are fixed
empirically ((Fox 2006) shows how can they be learned).
This model is used to compute the new probabilities
{p(in)t, p(out)t} using (1):
p(st) = at*p(ct|st)*Σ s’ ∈ {in, out} p(st|s’t-1)*p(s’t-1)
(1)
Results
with at as a normalization factor such that :
Σs ∈ {in, out} p(st) = 1
b) State estimation timelines
The evaluation of our system focusses on 2 key issues: the
efficacy of the adaptive behaviour for science and the
efficiency of the integration. The first requires us to test
that the system is able to predict the occurence (or absence)
of the pheonomena and adjust instrument and navigation
control accordingly. With the second, we are concerned
with reactivity in a real-world environment.
Our results are based on extensive simulations and multiple
sea trials over a period of a year, culminating in science
driven surveys. The surveys applied the integrated system
onboard a Dorado AUV, operating at a speed of 1.5 m/s, to
(2)
Equations (1) and (2) encapsulate HMM based belief. In so
doing, they express a constraint between current estimated
state, the last observation and the previous estimated state.
Fig 8a shows an HMM execution trace representation
where arrows represent the dependence between states and
observations. Fig 8b shows the timelines managing our
state estimation where arrows stand for constraint
propagation between tokens, indicating a strong similarity
between the two.
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This low number shows that the flexible partial plan
representation is incrementally refined and extended
throughout the mission without any need to re-plan. Where
re-planning was required, it was localized within the
Navigator and only impacted a small time horizon
validating our information model for partitioning of
control.
Related Work
Most plan execution techniques are coupled with state
estimation for fault detection isolation and recovery
(FDIR). A classic example of such an effort was the
Remote Agent Experiment (RAX) (Muscettola 1998 and
Rajan 2000) where onboard FDIR was used to trigger
replanning and projection of spacecraft state. RAX had
three distinct components with their own models with
correspondingly significant design and integration issues
(Bernard 2000). Like RAX, the Autonomous Sciencecraft
Experiment (Chien 2005) and other 3T architectures (Gat
1998) did not have a unified framework but relied on
loosely coupled systems with distinct representations.
Other efforts have focused on deriving models of time
series of state evolution of structured phenomena to build
Hidden Markov models of the environment (Lenser 2005)
even if the architecture does not explicitly deliberate as
outlined in our work. In (Sellner 2007) plans with durative
action can be repaired inline to deal with execution
anomalies using probabilistic approaches. Execution in this
work is however simulated within a less flexible plan
representation, while dealing with kernel density
estimations for duration prediction.
IDEA (Muscettola 2002, Finzi 2004) is a hybrid executive
that integrates planning and execution in a single
representational and computational framework with a
unified declarative model. Our approach is similar in its
formulation of a timeline-based representation and in its
use of constrained-based temporal planning to implement a
control loop. In contrast to IDEA, we separate
synchronization from deliberation. We further allow
synchronization to be interleaved with deliberation
permitting reaction times to extend to multiple time steps if
necessary. Moreover, the partitioning structure we provide
automatically applies rules for synchronization and
dispatch to co-ordinate among control loops and resolve
conflicts. These common patterns must be encoded
explicitly in an IDEA model, which we believe is
unnecessary and cumbersome.
Our work is further distinguished in the underwater domain
where popular techniques for AUV control have relied on
reactive approaches; see (Carreras 2006) for a survey.
ORCA (Turner 1991) and MCS (Palomeras 2007) while
integrating deliberation onboard do not deal with state
estimation or durative action representation. In the case of
ORCA, the case-based planner appears to have been used
in simulation only. Our work is the first integrated
Planning, Execution and Estimation framework deployed
for actual oceanographic exploration.
Fig 9: a) AUV survey volumes (blue) within the context of
regional seafloor and land topography. b) INL optical
backscattering intensity (x10-3 m-1 at 676 nm). c) Plume
detection probability (relative). Water sample locations
are indicated in b) and c).
detect and sample an INL in Monterey Canyon and a
coastal plume caused by outflow from the Elkhorn Slough
(Fig 9a). Water sampling employed a system developed
for AUV applications (Bird 2007). INL intensity, indicated
by optical data, is shown in Fig 9b along with locations of
water samples. Plume probability computed by the HMM
is shown in Fig 9c along with the water sample locations.
In both missions, our system was able to detect the feature
of interest, adapt the resolution of the survey consistent
with the finding (or lack thereof), and sample the feature.
In both missions the state estimator correctly excluded all
measurements that did not correspond to the feature of
interest, with no associated triggering of the water-sampler.
For the INL survey, the system generated and dispatched a
total of 188 commands over a period of 4 hours over the
canyon axis. Using conventional approaches, the operator
would have needed to specify all commands sent to the
VCS a priori without a guarantee of having sampled with
the precision expected for science.
For these sea trials, our system included 28 timelines
distributed across 3 reactors. The deliberative reactors ran
on a 367 MHz EPX-GX500 AMD Geode stack using Red
Hat Linux, with the VCS functional layer running on a
separate processor. Computational analyses of both these
runs indicate that approximately 20% of the time, the CPU
load was at about 5%. While the agent was always
synchronizing at 1Hz, and receiving observations from the
VCS at that rate, there was very little change in the plan
otherwise so the cost of synchronization is low with little
deliberation. Approximately 40% of the time, the CPU
utilization was around 10% when actively executing the
HMM. Once the vehicle was within the feature, the load
peaked at 25% and was dominated by the cost of
synchronization to evaluate spatial constraints and sampler
availability. The average CPU cost of deliberation was less
than 1% and 90% of the time there was no deliberation.
1323
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Conclusions and Future Work
Our field results to date are encouraging. Using a unified
representational and computational framework for
Estimation, Planning, and Execution has proved effective
for adaptive mission control. The integration of such
capabilities has resulted in precise observation and
sampling of dynamic processes that had not been done to
date. The scalability suggested by the CPU load data while
promising, requires more rigorous empirical evaluation.
The focus of our effort going forward will be in two key
areas: to investigate approaches for online learning and
longer-term projection for state estimation and to
investigate adaptive sampling within an information
theoretic framework. We expect this will allow us to
qualitatively characterize a dynamic feature in 4D (space
and time) by allowing the vehicle to spatially map and
sample the feature over an extended period of time. Our
longer-term vision is to extend our framework to control a
diverse range of mobile and immobile robotic assets that
can collaboratively help quantify a range of processes in
the ocean.
Acknowledgements
This research was supported by the David and Lucile
Packard Foundation. We thank NASA Ames Research
Center for making the EUROPA planner available, our
colleagues at MBARI for their help in integration and
deployment and Nicola Muscettola for detailed comments
on a late draft of this paper. We also thank the anonymous
reviewers with their insightful comments.
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